Abstract
This paper presents a new technique for providing the analysis and comparison of wiretap codes in the small blocklength regime over the binary erasure wiretap channel. A major result is the development of Monte Carlo strategies for quantifying a code's equivocation, which mirrors techniques used to analyze forward error correcting codes. For this paper, we limit our analysis to coset-based wiretap codes, and give preferred strategies for calculating and/or estimating the equivocation in order of preference. We also make several comparisons of different code families. Our results indicate that there are security advantages to using algebraic codes for applications that require small to medium blocklengths. © 2017 IEEE.

Abstract
The residential load of existing consumers can be increased significantly due to large-scale purchase of household appliances with high-energy consumption; consequently, changing the expansion plans of electrical distribution networks. In this paper, a spatial-Temporal model is proposed to estimate the load growth of distribution transformers owed for this kind of electrical appliances. In order to determine the location of inhabitants interested in buying these appliances, the proposed approach includes the socioeconomic characteristics of the consumers in a spatial form. After that, the number of appliances added each year is computed using a logistic regression. The results are the residential load curves of distribution transformers, including the additional yearly demand of the new appliances. These curves provide valuable information regarding the distribution network expansion planning.

Abstract

Abstract
In this paper it will be presented a proposal of a supervisory approach to be applied to the global localization algorithms in mobile robots. One of the objectives of this work is the increase of the robustness in the estimation of the robot's pose, favoring the anticipated detection of the loss of spatial reference and avoiding faults like tracking derail. The proposed supervisory system is also intended to increase accuracy in localization and is based on two of the most commonly used global feature based localization algorithms for pose tracking in robotics: Augmented Monte Carlo Localization (AMCL) and Perfect Match (PM). The experimental platform was a robotic wheelchair and the navigation used the sensory data from encoders and laser rangers. The software was developed using the ROS framework. The results showed the validity of the proposal, since the supervisor was able to coordinate the action of the AMCL and PM algorithms, benefiting the robot's localization system with the advantages of each one of the methods.

Abstract
Epilepsy being one of the most prevalent neurological disorders, affecting approximately 50 million people worldwide, and with almost 30-40% of patients experiencing partial epilepsy being nonresponsive to medication, epilepsy surgery is widely accepted as an effective therapeutic option. Presurgical evaluation has advanced significantly using noninvasive techniques based on video monitoring, neuroimaging, and electrophysiological and neuropsychological tests; however, certain clinical settings call for invasive intracranial recordings such as stereoelectroencephalography (SEEG), aiming to accurately map the eloquent brain networks involved during a seizure. Most of the current presurgical evaluation procedures focus on semiautomatic techniques, where surgery diagnosis relies immensely on neurologists' experience and their time-consuming subjective interpretation of semiology or the manifestations of epilepsy and their correlation with the brain's electrical activity. Because surgery misdiagnosis reaches a rate of 30%, and more than one-third of all epilepsies are poorly understood, there is an evident keen interest in improving diagnostic precision using computer-based methodologies that in the past few years have shown near-human performance. Among them, deep learning has excelled in many biological and medical applications, but has advanced insufficiently in epilepsy evaluation and automated understanding of neural bases of semiology. In this paper, we systematically review the automatic applications in epilepsy for human motion analysis, brain electrical activity, and the anatomoelectroclinical correlation to attribute anatomical localization of the epileptogenic network to distinctive epilepsy patterns. Notably, recent advances in deep learning techniques will be investigated in the contexts of epilepsy to address the challenges exhibited by traditional machine learning techniques. Finally, we discuss and propose future research on epilepsy surgery assessment that can jointly learn across visually observed semiologic patterns and recorded brain electrical activity.

Abstract
The growth of wireless sensor networks (WSN) has resulted in part from requirements for connecting sensors and advances in radio technologies. WSN nodes may be required to save energy and therefore wake up and sleep in a synchronized way. In this paper, we propose an application-driven WSN node synchronization mechanism which, by making use of cross-layer information such as application ID and duty cycle, and by using the exponentially weighted moving average (EWMA) technique, enables nodes to wake up and sleep without losing synchronization. The results obtained confirm that this mechanism maintains the nodes in a mesh network synchronized according to the applications they run, while maintaining a high packet reception ratio.